run.SSMimpute_unanimous_cpts: SSMimpute: state space model direct imputation on missing...

In Junzheshao5959/ssmimputedemo: SSM Imputation DEMO(Title Case)

SSMimpute: state space model direct imputation on missing data in covariates

Description

SSMimpute: state space model direct imputation on missing data in covariates

Usage

run.SSMimpute_unanimous_cpts(
  data_ss_ori,
  formula_var,
  ss_param_temp,
  initial_imputation_option = "StructTS",
  estimate_convergence_cri = 0.01,
  lik_convergence_cri = 0.01,
  stepsize_for_newpart = 1/3,
  max_iteration = 100,
  cpt_learning_param = list(cpt_method = "mean", burnin = 1/10, mergeband = 20,
    convergence_cri = 15),
  cpt_initial_guess_option = "ignore",
  dlm_option = "smooth",
  m = 5,
  seed = 1,
  printFlag = T
)

Arguments

data_ss_ori	contains all information, and only selected variables in formula_var enters the statespace model
formula_var	select variables from <data_ss_ori> into the state space model
ss_param_temp	A list of parameters, details below
initial_imputation_option	for the first iteration of imputing missing y, choose StructTS or others, and can't be "ignore"
estimate_convergence_cri	critic value for convergence check, default 0.01
lik_convergence_cri	critic value for convergence check, default 0.01
stepsize_for_newpart	stepsize specified, default 1/3
max_iteration	max iteration, default 100
cpt_learning_param	<cpt_method> either "mean" or "meanvar"
cpt_initial_guess_option	option for initially learning cpts in preparation period
dlm_option	choose between smooth or filter
m	number of draws for multiple imputation
seed	random seed
printFlag	whether we need to print the Flag plots.

Details

<m0> initial values for states <C0>: initial values for variance of states <inits>: initial values for the estimating of all NA terms, via maximizing likelihood

Value

A list

Junzheshao5959/ssmimputedemo documentation built on Aug. 27, 2022, 8:49 a.m.